The history of genomics began in the 80s. For almost half a century, scientists have been studying the structure and functions of the genome, the interaction of genes with each other and with the environment. And the increasing availability of sequencing technologies every year expands the possibilities of such studies. Depending on the goals and methods, there are several key areas of genomics, which we will discuss below.
Functional genomics reveals the whole way of realization of the genome: from genes to certain proteins and features of the organism. Everything is complicated by the fact that one protein can affect several traits at once, several proteins can affect one trait, and one gene (thanks to alternative splicing) is responsible for several proteins.
How to describe the three-dimensional structure of a protein, knowing the base sequence of the gene that encodes it? This is where structural genomics comes into play. A big role in research is played by the fact that scientists determine the structures encoded by the genome, rather than focusing on one specific protein. In particular, already studied gene sequences and related protein structures help in this.
If the genome is the sum of all the genes of an organism, then the epigenome is a set of epigenetic modifications – factors that do not change the sequence of bases in DNA, but on which the work of genes depends. These include, for example, methylation, histone modifications, acetylation, phosphorylation, etc.
Epigenetic modifications are involved in cell differentiation and carcinogenesis. Epigenomics is also used in the context of the biology of aging: in older people, changes in methylation profiles are observed. Data on modifications are obtained in various ways: some types of sequencing (bisulfite sequencing, PacBio, ONT), chromatin immunoprecipitation (ChIP-on-chip, ChIP-Seq), hybridization in situmethylation-sensitive restriction enzyme, DNA adenine methyltransferase identification (DamID).
Comparative and evolutionary genomics
In this area, scientists work with large sets of genomes of different organisms, compare their organization and composition, and identify genomic rearrangements. A common tool here is pangenomes – the union of all the genes of a group of organisms. Modeling of evolution, functional annotation of a gene by sequence, search for homologous genes and blocks of synteny, search for molecular markers of selection are examples of problems in comparative genomics.
Here they study the structure of the population’s gene pool, its dynamics over time and the causes of variability, look for polymorphisms, establish links between the genetic variant, phenotype and habitat of the population. The gene pools of many peoples of the world have already been described. The main data for analysis are genetic markers and their analogues, including mitochondrial DNA and Y-chromosome sequences, complete genome sequences, and anthropological characteristics.
Metagenomics uses the genetic materials of organisms obtained from environmental samples. Scientists analyze the species composition, phylogeny, and diversity of the community in a sample using sequencing data. More often, not the complete genome is sequenced, but the sequence of 16S rRNA in prokaryotes and 18S rRNA in eukaryotes.
The genome-wide association search (GWAS) method involves the study of genetic variants in different people in order to find out if any variant is associated with any trait. In medicine, this helps to find genetic markers of diseases. Scientists are looking for single nucleotide polymorphisms (SNPs), chromosomal rearrangements (inserts, deletions, inversions, etc.), as well as copy number variation (CNV) in the genome arising from chromosomal rearrangements.
These are far from all the methods that are used in modern genomics. Initially, genomic research was aimed at determining DNA sequences, but over several decades expanded to a more functional level – the study of the gene pools of entire populations, expression profiles and the accumulation of a huge amount of genetic information. And the ability to process data has become a key skill in this area.
Helped to prepare the text Julia Antonenkova.